Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951 [DOI: 10.4240/wjgs.v18.i2.114951]
Corresponding Author of This Article
Ju Wu, Department of General Surgery, Zhongshan Hospital Affiliated to Dalian University, No. 6 Jiefang Street, Zhongshan District, Dalian 116001, Liaoning Province, China. wuju@s.dlu.edu.cn
Research Domain of This Article
Computer Science, Interdisciplinary Applications
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Feb 27, 2026 (publication date) through Feb 26, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Surgery
ISSN
1948-9366
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951 [DOI: 10.4240/wjgs.v18.i2.114951]
Ya-Na Lü, Dong Liu, Shuai Tao, Shu-Juan Yu, Hui-Ling Yuan, School of Information Engineering, Dalian University, Dalian 116622, Liaoning Province, China
Ju Wu, Department of General Surgery, Zhongshan Hospital Affiliated to Dalian University, Dalian 116001, Liaoning Province, China
Author contributions: Lü YN contributed to conceptualization, methodology, and supervision; Liu D and Tao S contributed to formal analysis, software, validation, and visualization; Wu J, Yu SJ and Yuan HL contributed to data curation, investigation, and resources. All authors contributed to writing - original draft, review and editing, and approved the final manuscript.
Institutional review board statement: This study has been approved by Institutional Review Board of the Zhongshan Hospital Affiliated to Dalian University (Approval No. KY2023-002-2).
Informed consent statement: All study participants and their legal guardians provided written informed consent before recruitment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Corresponding author: Ju Wu, Department of General Surgery, Zhongshan Hospital Affiliated to Dalian University, No. 6 Jiefang Street, Zhongshan District, Dalian 116001, Liaoning Province, China. wuju@s.dlu.edu.cn
Received: October 11, 2025 Revised: December 2, 2025 Accepted: January 8, 2026 Published online: February 27, 2026 Processing time: 147 Days and 2.4 Hours
Abstract
BACKGROUND
Accurate prediction of postoperative survival is crucial for the personalized management of gastric cancer. However, the development of robust predictive models is often constrained by incomplete clinical data, while their clinical utility is limited by poor interpretability and the absence of practical applications.
AIM
To develop an interpretable machine learning model for predicting 3-year survival following gastric cancer surgery. A novel data imputation method was proposed to handle missing values, and a user-friendly online tool was developed to facilitate clinical decision-making.
METHODS
A retrospective analysis was conducted on a group of 304 patients with gastric adenocarcinoma. A hybrid imputation method (HDI-MF-Gower) was developed and compared against conventional techniques. Key prognostic factors were identified by integrating least absolute shrinkage and selection operator regression with the Boruta algorithm. Subsequently, ten machine learning models were trained and validated.
RESULTS
The proposed HDI-MF-Gower method demonstrated superior imputation accuracy. Seven features were selected for the final model. The extra trees classifier achieved the best performance on the independent validation set, with an area under the curve of 0.853 and an accuracy of 0.772. The optimal model was interpreted using SHapley Additive exPlanations analysis and deployed as an online prediction tool.
CONCLUSION
A robust and interpretable predictive model integrating advanced data imputation was successfully developed. The deployed tool facilitates individualized prognostic assessment and shows potential for enhancing personalized treatment planning in gastric cancer.
Core Tip: This study developed a novel hybrid imputation method (HDI-MF-Gower) to handle missing clinical data. We then built and validated a robust machine learning model (extra trees classifier) for predicting postoperative 3-year survival in gastric cancer patients. The model demonstrated high performance (area under the curve of 0.853), and its clinical application is facilitated by interpretable SHapley Additive exPlanations analysis and a user-friendly online prediction tool, aiding personalized treatment planning.
